AI correctly predicted the Banga win.
The match finished 1–3, validating the model's directional assessment.
Tracked markets vs full-time result
Each row compares the pre-match model lean to the full-time result.
- Market Prediction Result Outcome
- Over / Under 2.5 Under 2.5 Over 2.5 (4 goals) ✖ Incorrect
- Both Teams To Score BTTS Yes Yes ✔ Correct
- 1X2 Banga Banga ✔ Correct
- Correct Score Insights 1-1, 0-1, 1-0, 1-2, 2-1 1-3 ✖ Incorrect
Model vs Closing Market
| Outcome | Model | Closing Market | Difference | Signal |
|---|---|---|---|---|
| Šiauliai | 34.7% | 23.9% | +10.8 pp | Model Edge |
| Draw | 29.6% | 24.6% | +5.1 pp | Model Higher |
| Banga | 35.6% | 51.5% | -15.8 pp | Market Higher |
The closing market estimates Banga's win probability at 51.5%, compared with the model's estimate of 35.6%, a difference of 15.8 percentage points. This highlights a disagreement between the model and market consensus, without indicating which view is ultimately correct.
Model probabilities are generated from the statistical xG model using a Poisson distribution. Closing market probabilities are derived from consensus closing 1X2 odds after margin removal. Values represent implied probabilities rather than betting recommendations. Closing snapshot: PRE1.
After full time, the model's directional lean matched the result (Banga win 1–3).
Market Assessment
The market is materially more optimistic about Banga than the current fair estimate.
- Investors may be incorporating information not fully reflected in the baseline model.
- Tournament-specific context can shift market pricing.
Post Match Insights
What worked
- Expected goals projected a high-scoring match (ΣxG 2.60) — 4 goals materialised
- Both Teams To Score (Yes) matched the full-time result
What failed
- Over / Under 2.5: model leaned Under 2.5; match finished Over 2.5 (4 goals)
- Exact score: outside the model's top score bins
Market lesson
Large model–market gaps do not automatically mean the market is right. Here the closing market priced Banga higher (51.4% vs model 35.6%, 15.8 pp), but the model's lean was validated (Banga win 1–3).
Prediction Timeline
How this prediction moved from forecast to full-time review.
-
Aug 03, 2026 · 15:44 UTC Forecast generated
- Model 1X2 · Šiauliai 34.7% · Draw 29.7% · Banga 35.6%
- xG · Šiauliai 1.29 — Banga 1.31
-
Aug 03, 2026 · 15:14 UTC Opening odds snapshot PRE30
- 1X2 odds · Šiauliai 3.87 · Draw 3.77 · Banga 1.80
- Implied 1X2 · Šiauliai 23.9% · Draw 24.6% · Banga 51.5%
- Bookmaker · Pinnacle
-
Aug 03, 2026 · 15:44 UTC Closing snapshot recorded PRE1
- 1X2 odds · Šiauliai 3.87 · Draw 3.77 · Banga 1.80
- Implied 1X2 · Šiauliai 23.9% · Draw 24.6% · Banga 51.5%
- Bookmaker · Pinnacle
-
Aug 03, 2026 · 15:45 UTC Kickoff
-
FT Full-time result Banga win · 1–3
-
FT Prediction validated Directional lean matched full-time result
-
Archived Prediction review
Historical Snapshot
Frozen at kickoff — the model output as it stood before the match started.
Pre-match metrics (historical context)
- Validation: Warning
- Large market gap (16 pp)
- No strong statistical edge
- Pricing remains divergent
- Validation warning
Validation Report
Immutable SnapshotModel Performance
This prediction contributes to:
- Primary Bets ROI (180d): -100.0%
Review FAQ
- How accurate was the prediction?
- This page grades directional markets (1X2, Over/Under 2.5, BTTS) against the full-time result. The prediction grade reflects how many of those tracked markets matched reality.
- What does xG Accuracy measure?
- xG Accuracy compares the model's pre-match expected-goals profile to the actual scoreline — not whether every market hit. A strong directional review can coexist with a moderate xG accuracy score.
- Why wasn't the exact score predicted?
- Correct-score outcomes are low-probability tails even when the model reads the match profile well. We highlight top score bins for context; missing the exact line does not invalidate a directional review.
- Does this improve the AI record?
- Each finished match is logged in our validation pipeline. Aggregated hit rates and CLV studies are published separately — this page is the per-match audit trail.
Predictions are for informational purposes only. Always gamble responsibly and within your limits. Past performance does not guarantee future results.
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A Lyga — Standings
| # | TEAM | MP | W | D | L | PTS |
|---|---|---|---|---|---|---|
| 1 | Kauno Žalgiris | 28 | 14 | 9 | 5 | 51 |
| 2 | Suduva Marijampole | 28 | 13 | 11 | 4 | 50 |
| 3 | TransINVEST Vilnius | 28 | 13 | 6 | 9 | 45 |
| 4 | FK Zalgiris Vilnius | 28 | 13 | 5 | 10 | 44 |
| 5 | Banga | 28 | 11 | 8 | 9 | 41 |
| 6 | Džiugas Telšiai | 28 | 10 | 8 | 10 | 38 |
| 7 | Panevėžys | 28 | 9 | 5 | 14 | 32 |
| 8 | Hegelmann Litauen | 29 | 4 | 14 | 11 | 26 |
| 9 | Šiauliai | 29 | 3 | 8 | 18 | 17 |
| 10 | FK Trakai | 0 | 0 | 0 | 0 | 0 |
| # | TEAM | MP | GS | GC | +/- | PTS |
|---|---|---|---|---|---|---|
| 1 | Kauno Žalgiris | 28 | 56 | 20 | +36 | 51 |
| 2 | TransINVEST Vilnius | 28 | 43 | 35 | +8 | 45 |
| 3 | FK Zalgiris Vilnius | 28 | 41 | 34 | +7 | 44 |
| 4 | Suduva Marijampole | 28 | 39 | 25 | +14 | 50 |
| 5 | Džiugas Telšiai | 28 | 36 | 40 | -4 | 38 |
| 6 | Banga | 28 | 32 | 28 | +4 | 41 |
| 7 | Hegelmann Litauen | 29 | 31 | 48 | -17 | 26 |
| 8 | Panevėžys | 28 | 29 | 43 | -14 | 32 |
| 9 | Šiauliai | 29 | 24 | 58 | -34 | 17 |
| 10 | FK Trakai | 0 | 0 | 0 | 0 | 0 |
